The Dimensions of Reputation in Electronic Markets

We analyze how di erent dimensions of a seller's reputation a ectpricing power in electronic markets. We do so by using text miningtechniques to identify and structure dimensions of importance fromfeedback posted on reputation systems, by aggregating and scoring thesedimensions based on the sentiment they contain, and using them toestimate a series of econometric models associating reputation withprice premiums. We nd that di erent dimensions do indeed a ect pricingpower di erentially, and that a negative reputation hurts more than apositive one helps on some dimensions but not on others. We provide therst evidence that sellers of identical products in electronic markets dierentiate themselves based on a distinguishing dimension of strength,and that buyers vary in the relative importance they place on di erentful lment characteristics. We highlight the importance of textualreputation feedback further by demonstrating it substantially improvesthe performance of a classi er we have trained to predict future sales.This paper is the rst study that integrates econometric, text miningand predictive modeling techniques toward a more complete analysis ofthe information captured by reputation systems, and it presents newevidence of the importance of their e ective and judicious design.